---
title: "SuperWorld Plot Sales"

output: 
  flexdashboard::flex_dashboard:
    social: menu
    source_code: embed
    theme: yeti

---

Cumulative Plot Sales
=====================================

Inputs {.sidebar}
-------------------------------------

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
library(leaflet)
library(leaflet.extras)
library(sf)
library(tidyverse)
library(rnaturalearth)
library(rnaturalearthdata)
library(plotly)
library(usmap)
library(lubridate)

plots_sold = read_csv("C:/Users/rebec/SuperWorld_Plot_Recommendation/data/plots_sold.csv")[-1]
plots_sold$code = toupper(plots_sold$code)

us_plots = plots_sold[which(plots_sold$code == "US"),]
us_address = us_plots$address

state = c()
for (i in 1:length(us_address)){
  add = tail(unlist(str_split(us_address[i], pattern = ", ")), 2)[1]
  add = gsub(' [[:digit:]]+', '', add)
  state = c(state, add)
}

us_plots = cbind(us_plots, state) 

state_data = data.frame(state) %>%
  group_by(state) %>%
  summarise(sold = n())

```

*Total Plot Sales:*

```{r}
nrow(plots_sold)
```




*Top 10 Countries:*

```{r}
plots_sold %>% 
  group_by(country) %>% 
  summarise(`plots sold` = n()) %>% 
  arrange(-`plots sold`) %>% 
  head(10) %>% 
  knitr::kable()
```




*Top 10 US States:*

```{r}
state_data %>% 
  summarise(state, `plots sold` = sold) %>% 
  arrange(-`plots sold`) %>% 
  head(10) %>% 
  knitr::kable()
```






Column {data-width=800}
-------------------------------------
    
### Plots Sold Across the World

```{r warning=FALSE, message=FALSE}
world = ne_countries(scale = "medium", returnclass = "sf")
df = st_sf(merge(plots_sold, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf"))

df_plot = df %>%
    group_by(country, code) %>%
    summarise(sold = n()) %>%
    mutate(sold = ifelse(is.na(country), 0, sold)) %>%
    ggplot() +
    geom_sf(aes(fill = sold))+
    scale_fill_gradient(trans = "log") +
    geom_sf_text(aes(label = code), size = 2) +
    theme(axis.title.x=element_blank(),
          axis.title.y=element_blank(),
          legend.title = element_text("Plots Sold")) +
    labs(caption = "Sold values are in log scale") +
    guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10))

# df2 = df %>%
#     group_by(country, code) %>%
#     summarise(sold = n()) %>%
#     mutate(sold = ifelse(is.na(country), 0, sold))

# plot(df2["sold"], logz = TRUE, main = NULL, key.pos = 4)

p = ggplotly(df_plot) %>% 
  layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", 
                            showarrow = F, xref='paper', yref='paper', 
                            xanchor='right', yanchor='auto', xshift=0, yshift=-1,
                            font=list(size=15)),
         xaxis = list(autorange = TRUE),
         yaxis = list(autorange = TRUE)
         )
p
```


   
Row 
-------------------------------------
   
### Plot Locations

```{r}
leaflet(plots_sold) %>% 
  addTiles() %>% 
  addCircles(lng = ~lon, lat = ~lat) %>% 
  setView(lat = 37.0902, lng = -95.7129, zoom = 4)
```   
 
### Plots Sold in the US

```{r}
us = plot_usmap(data = state_data, values = "sold", regions = "states") +
  theme(legend.position = "right") +
  scale_fill_continuous(name = "Plots Sold")
  
p_us = ggplotly(us)
p_us
```
    



World Plots
=====================================

Column {data-width=200}
-------------------------------------

### Cumulative Plot Sales 

```{r}
plots_sold %>% 
  group_by(country) %>% 
  summarize(sold = n()) %>% 
  arrange(-sold) %>% 
  knitr::kable()
```

Column {.tabset}
-------------------------------------

### Day

```{r}
plots_today = plots_sold %>% 
  mutate(days = interval(date, today()) %/% days(1)) %>%
  filter(days < 1)
df_today = st_sf(merge(plots_today, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf"))

plot = df_today %>%
    group_by(country, code) %>%
    summarise(sold = n()) %>%
    mutate(sold = ifelse(is.na(country), 0, sold)) %>%
    ggplot() +
    geom_sf(aes(fill = sold))+
    scale_fill_gradient(trans = "log") +
    geom_sf_text(aes(label = code), size = 2) +
    theme(axis.title.x=element_blank(),
          axis.title.y=element_blank(),
          legend.title = element_text("Plots Sold")) +
    labs(caption = "Sold values are in log scale") +
    guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10))

ggplotly(plot) %>% 
  layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", 
                            showarrow = F, xref='paper', yref='paper', 
                            xanchor='right', yanchor='auto', xshift=0, yshift=-1,
                            font=list(size=15)),
         xaxis = list(autorange = TRUE),
         yaxis = list(autorange = TRUE)
         )
```

### Week

```{r}
plots_week = plots_sold %>% 
  mutate(days = interval(date, today()) %/% days(1)) %>%
  filter(days < 7)
df_week = st_sf(merge(plots_week, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf"))

plot = df_week %>%
    group_by(country, code) %>%
    summarise(sold = n()) %>%
    mutate(sold = ifelse(is.na(country), 0, sold)) %>%
    ggplot() +
    geom_sf(aes(fill = sold))+
    scale_fill_gradient(trans = "log") +
    geom_sf_text(aes(label = code), size = 2) +
    theme(axis.title.x=element_blank(),
          axis.title.y=element_blank(),
          legend.title = element_text("Plots Sold")) +
    labs(caption = "Sold values are in log scale") +
    guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10))

ggplotly(plot) %>% 
  layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", 
                            showarrow = F, xref='paper', yref='paper', 
                            xanchor='right', yanchor='auto', xshift=0, yshift=-1,
                            font=list(size=15)),
         xaxis = list(autorange = TRUE),
         yaxis = list(autorange = TRUE)
         )
```

### Month

```{r}
plots_month = plots_sold %>% 
  mutate(days = interval(date, today()) %/% days(1)) %>%
  filter(days < 30)
df_month = st_sf(merge(plots_month, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf"))

plot = df_month %>%
    group_by(country, code) %>%
    summarise(sold = n()) %>%
    mutate(sold = ifelse(is.na(country), 0, sold)) %>%
    ggplot() +
    geom_sf(aes(fill = sold))+
    scale_fill_gradient(trans = "log") +
    geom_sf_text(aes(label = code), size = 2) +
    theme(axis.title.x=element_blank(),
          axis.title.y=element_blank(),
          legend.title = element_text("Plots Sold")) +
    labs(caption = "Sold values are in log scale") +
    guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10))

ggplotly(plot) %>% 
  layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", 
                            showarrow = F, xref='paper', yref='paper', 
                            xanchor='right', yanchor='auto', xshift=0, yshift=-1,
                            font=list(size=15)),
         xaxis = list(autorange = TRUE),
         yaxis = list(autorange = TRUE)
         )
```

### Year

```{r}
plots_year = plots_sold %>% 
  mutate(days = interval(date, today()) %/% days(1)) %>%
  filter(days < 365)
df_year = st_sf(merge(plots_year, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf"))

plot = df_year %>%
    group_by(country, code) %>%
    summarise(sold = n()) %>%
    mutate(sold = ifelse(is.na(country), 0, sold)) %>%
    ggplot() +
    geom_sf(aes(fill = sold))+
    scale_fill_gradient(trans = "log") +
    geom_sf_text(aes(label = code), size = 2) +
    theme(axis.title.x=element_blank(),
          axis.title.y=element_blank(),
          legend.title = element_text("Plots Sold")) +
    labs(caption = "Sold values are in log scale") +
    guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10))

ggplotly(plot) %>% 
  layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", 
                            showarrow = F, xref='paper', yref='paper', 
                            xanchor='right', yanchor='auto', xshift=0, yshift=-1,
                            font=list(size=15)),
         xaxis = list(autorange = TRUE),
         yaxis = list(autorange = TRUE)
         )
```

### Total

```{r}
p
```

US Plots 
=====================================

Column {data-width=200}
-------------------------------------

### Cumulative US Plot Sales 

```{r}
us_plots %>% 
  group_by(state) %>% 
  summarize(sold = n()) %>% 
  arrange(-sold) %>% 
  knitr::kable()
```


Column {.tabset}
-------------------------------------

### Day

```{r}


us_today = us_plots%>% 
  mutate(days = interval(date, today()) %/% days(1)) %>%
  filter(days < 1) %>% 
  group_by(state) %>% 
  summarise(sold = n())
us_today = plot_usmap(data = us_today, values = "sold", regions = "states") +
  theme(legend.position = "right") +
  scale_fill_continuous(name = "Plots Sold")
  
ggplotly(us_today)


```

### Week

```{r}
us_week = us_plots%>% 
  mutate(days = interval(date, today()) %/% days(1)) %>%
  filter(days < 7) %>% 
  group_by(state) %>% 
  summarise(sold = n())
us_week = plot_usmap(data = us_week, values = "sold", regions = "states") +
  theme(legend.position = "right") +
  scale_fill_continuous(name = "Plots Sold")
  
ggplotly(us_week)
```

### Month

```{r}
us_month = us_plots%>% 
  mutate(days = interval(date, today()) %/% days(1)) %>%
  filter(days < 30) %>% 
  group_by(state) %>% 
  summarise(sold = n())
us_month = plot_usmap(data = us_month, values = "sold", regions = "states") +
  theme(legend.position = "right") +
  scale_fill_continuous(name = "Plots Sold")
  
ggplotly(us_month)
```

### Year

```{r}
us_year = us_plots%>% 
  mutate(days = interval(date, today()) %/% days(1)) %>%
  filter(days < 365) %>% 
  group_by(state) %>% 
  summarise(sold = n())
us_year = plot_usmap(data = us_year, values = "sold", regions = "states") +
  theme(legend.position = "right") +
  scale_fill_continuous(name = "Plots Sold")
  
ggplotly(us_year)
```

### Total

```{r}
p_us
```